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The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage.
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Radix+ : High‐throughput georeferencing and data ingestion over voluminous and fast‐evolving phenotyping sensor data
Summary Remote sensing of plant traits and their environment facilitates non‐invasive, high‐throughput monitoring of the plant's physiological characteristics. However, voluminous observational data generated by such autonomous sensor networks overwhelms scientific users when they have to analyze the data. In order to provide a scalable and effective analysis environment, there is a need for storage and analytics that support high‐throughput data ingestion while preserving spatiotemporal and sensor‐specific characteristics. Also, the framework should enable modelers and scientists to run their analytics while coping with the fast and continuously evolving nature of the dataset. In this paper, we present Radix+ , a high‐throughput distributed data storage system for supporting scalable georeferencing, and interactive query‐based spatiotemporal analytics with trackable data integrity. We include empirical evaluations performed on a commodity machine cluster with up to 1 TB of data. Our benchmarks demonstrate subsecond latency for majority of our evaluated queries and improvement in data ingestion rate over systems such as Geomesa.
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- Award ID(s):
- 1931363
- PAR ID:
- 10448754
- Date Published:
- Journal Name:
- Concurrency and Computation: Practice and Experience
- Volume:
- 35
- Issue:
- 8
- ISSN:
- 1532-0626
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/cpe.7484
